Accordingly, we need to determine the concentration parameters, which are closely related to the uncertainty. We elaborate on the Subjective logic [1]}, which defines a theoretical framework for obtaining the probabilities (belief masses) of different classes and overall uncertainty (uncertainty mass) of the multi-classification problem based on the evidence collected from data. Note that evidence refers to the metrics collected from the input to support the classification (step [baseline=(char.base)]
shape=circle,draw,inner sep=0.5pt] (char) 1; in Fig. REF ) and is closely related to the concentration parameters of Dirichlet distribution. Specifically, for the \(K\)  classification problems, subjective logic tries to assign a belief mass to each class label and an overall uncertainty mass to the whole frame based on the evidence. Accordingly, for the \(v^{th}\)  view, the \(K+1\)  mass values are all non-negative and their sum is one:
\(u^v+\sum _{k=1}^{K} b_{k}^{v}=1,\) 
